Department of Pattern Recognition

(eng): Paint manufacturers strive to introduce unique visual effects to coatings in order to visually communicate functional properties of products using value-added, customized design. However, these effects often feature complex angularly dependent spatially-varying behavior, thus representing a challenge in digital reproduction. In this paper we analyze several approaches to capturing spatially-varying appearance of effect coatings. We compare a baseline approach based on bidirectional texture function (BTF) with four variants of half-difference parameterization. Through a psychophysical study we identify minimal sampling along individual dimensions of this parametrization. We conclude that bivariate representations preserve visual fidelity of effect coatings, while in contrast to BTF, better characterizing near-specular behavior and significantly restricting number of captured images.